Self Positioning Of A Wireless Station

ABSTRACT

A self-positioning mechanism is provided that determines and tracks the position of an access point in real time. A location unaware access point determines its location from the locations of location aware stations. The location is determined based on a predicted estimate which is updated based on measured values of the locations of the location aware stations over a time period. The movement of the location is then tracked based on the differences between range measurements of the location aware stations.

RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.12/950,950 entitled “Self-Positioning Of A Wireless Station” filed Nov.19, 2010.

BACKGROUND

Embodiments of the subject matter relate generally to communications,and more particularly to techniques for determining and continuouslytracking the position of a wireless station.

There is an increased trend for wireless devices to employlocation-based services. These location-based services provide a user ofa wireless device with information accessible through a wirelessnetwork. The location-based service relies on the geographical positionof the wireless device. Examples of such information can be the localweather information, driving directions, entertainment services, and thelike. The geographical location of a wireless device is based on a knowncoordinate system (e.g., WGS84) that is used in a particular positioningsystem. Examples of such positioning systems include the globalpositioning system (GPS) and the terrestrial positioning systems used incellular networks (e.g., GSM).

A typical wireless network employs one or more access points to connectthe wireless devices through a wired medium, such as the Ethernet, to alarger communications network, such as the Internet. Each access pointhas a location that is associated with a particular positioning system.The location can be a GPS position, a GSM location, or the like. Mostaccess points are not aware of their locations and obtain it throughmanual configuration.

Furthermore, access points are located in a variety of places, such aspublic parks, restaurants, private business, airports, libraries, etc.The location of some of these access points can be stationary. In somecases, the location of an access point changes as the access pointmoves. This movement requires that the location of the access point beupdated. Accordingly, a need arises for automatically determining andtracking the location of an access point.

SUMMARY

Various embodiments are presented of a self-positioning apparatus andmethod that enables an access point to determine and track its locationwithin a positioning system, in real time. An access point has alocation that is based on a coordinate system within a positioningsystem. The positioning system can be a wireless communication system ora satellite constellation. An access point, not knowing its location, isreferred to as a location unaware access point. The location unawareaccess point can learn its position using information obtained fromlocation aware stations that are within range of the location unawareaccess point.

The access point learns its position by utilizing an Extended KalmanFilter (EKF). An EKF estimates the location, or state, of adiscrete-time controlled system that is governed by a non-linearstochastic differentiable function from noisy measurements. The EKFstarts with an initial state estimate and covariance that is derivedfrom the locations of location aware stations for an initial time step.The initial state estimate is used to determine a predicted state and apredicted covariance. The predicted state estimate and covariance arerefined using range measurements of the location aware stations at eachtime step thereby producing an output state and output covariance ateach time step. The predicted state estimate and the output state arecomputed for each time step until the difference between them reaches auser-defined error tolerance.

The access point tracks movement from its position by comparing a teststatistic with a threshold at a particular confidence level. When themovement is confirmed, the access point re-computes its position.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter disclosed is illustrated by way of example, and notby way of limitation, in the figures of the accompanying drawings inwhich the like reference numerals refer to similar elements and inwhich:

FIG. 1 is a block diagram illustrating an exemplary configuration of acommunications system in accordance with some embodiments;

FIG. 2 is a block diagram illustrating an exemplary configuration of anaccess point in accordance with some embodiments;

FIG. 3 is a block diagram illustrating an exemplary configuration of alocation server in accordance with some embodiments;

FIG. 4 is an exemplary flow diagram illustrating a method fordetermining and tracking a location of a wireless station in accordancewith some embodiments;

FIG. 5 is an exemplary flow diagram illustrating a method fordetermining a location of a wireless station in accordance with someembodiments;

FIG. 6 is an exemplary flow diagram illustrating a method for refining alocation estimate in accordance with some embodiments; and

FIG. 7 is an exemplary flow diagram illustrating a method for detectingthe movement of a wireless station in accordance with some embodiments.

DETAILED DESCRIPTION

FIG. 1 illustrates an exemplary configuration of a communications system100 for use in some embodiments. There is shown a communications system100 having one or more access points 102 in communication with one ormore stations 104 through a wireless communication system 110. Eachaccess point 102 is coupled to a communications network 106, through awired link 114. A location server 108 can also be coupled to thecommunications network 106. Each of the access points 102 and stations104 can be coupled to a satellite system 112.

An access point 102 is a bridge having the capability to connectwireless devices with a communications network 106. Typically, theaccess point 102 is coupled to the communications network 106 through awired link, such as coaxial cable twisted pair using Ethernet networkingprotocols. The communications network 106 can be any type of networkstructured in any configuration, such as, without limitation, theInternet.

Each station 104 can be any type of wireless device, such as withoutlimitation, cellular phones, PDAs, personal computers, servers, any typeof computing device, and the like. Each access point 102 communicateswith one or more stations 104 through a wireless communication system110. In some embodiments, an access point 102 is associated with alocation within the coordinate system used in a cellular network. Theaccess point 102 can utilize the technology described herein todetermine its location within that coordinate system without directlyreceiving it from the wireless communication system 110.

In a wireless communication system operating in accordance with the IEEE802.11 standard, access points and stations communicate throughtransmissions referred to as frames. Access points periodically transmitbeacon frames to announce their existence and to relay informationregarding their identification and location. Stations typically monitorthe radio channels and listen for the beacon frames or beacons that arewithin range. A station associates with an access point to obtain accessto the communications network 106 and the services provided by it. Astation uses an association request frame to associate with an accesspoint. The access point sends an association response frame back to thestation containing an acceptance or rejection of the associationrequest.

A station or access point transmits a probe request frame when it needsinformation from another station or access point. The access point orstation responding to the probe request frame responds by sending aprobe response frame containing the requested information.

Each access point 102 and station 104 can communicate with one or moresatellites in the satellite system (also referenced as a globalnavigation satellite system (GNSS)) 112, such as GPS, GLONASS, and thelike. If an access point has a GPS receiver, the access point candetermine its location from the GNSS. However, if the GPS receiver isnot able to receive signals, the access point can utilize the technologydescribed herein to determine its location.

FIG. 2 shows an exemplary configuration of an access point 102. There isshown an access point 102 having at least one antenna 120 coupled to atransmitter/receiver unit 122, a network interface 132, a controller134, and a memory 136. The antenna 120 is coupled to thetransmitter/receiver unit 122 and is used to transmit and receive WiFisignals. The transmitter/receiver unit 122 can include satellitecircuitry (not shown) for receiving and transmitting signals to thesatellite system 112 (FIG. 1). In addition, the transmitter/receiverunit 122 can include WiFi circuitry (not shown) for receiving andtransmitting, through the antenna 120, wireless signals to the wirelesscommunication system 110 (FIG. 1). The network interface 132 is used tocommunicate through the wired link 114 with the communications network106, the controller 134 manages and controls processing within theaccess point, and the memory 136 is used to store instructions and dataused in the operation of the access point 102.

The memory 136 is a non-transitory computer readable medium that canstore executable procedures, code, applications, and data. It can be anytype of memory device (e.g., random access memory, read-only memory,flash memory, etc.), magnetic storage, volatile storage, non-volatilestorage, optical storage, DVD, CD, and the like. The memory 136 cancontain instructions and data as follows:

-   -   an operating system 138;    -   a self-position location procedure 140;    -   range measurement data from location aware stations 142;    -   location data from location aware stations 144; and    -   other applications and data 146.

Referring back to FIG. 1, a location server 108 is a computing devicethat is in communication with the access points 102 (and thus stations104) through the communications network 106. The location server 108 canbe any type of computing device, such as without limitation, server,personal computer, PDA, cell phone, etc. The location server 108 can beused to store the locations of the various access points and stations ina reference database and to provide additional computing and resourcesas needed. Referring to FIG. 3, the location server 108 can include, atleast, an antenna 150 coupled to a transmitter/receiver unit 152, aprocessor 162 that controls the operation of the location server 108, anetwork interface 164 for communicating with the communications network106, and a memory 154. The memory 154 is a non-transitory computerreadable medium that can store executable procedures, code,applications, and data. It can be any type of memory device (e.g.,random access memory, read-only memory, flash memory, etc.), magneticstorage, volatile storage, non-volatile storage, optical storage, DVD,CD, and the like. The memory 154 can contain instructions and data asfollows:

-   -   an operating system 156;    -   a reference database 158 that stores the locations of the        various stations and access points and/or measurement data; and    -   other applications and data 160.

Attention now turns to embodiments of the self-positioning methodologyutilized by the communications system described above.

The self-positioning process uses an estimation technique referred to asan Extended Kalman Filter (EKF) to estimate the location of an accesspoint. An EKF estimates the state of a discrete-time controlled systemthat is governed by a non-linear stochastic differentiable function fromnoisy measurements. In essence, the methodology estimates the state ofthe system, which cannot be measured, by measuring a variable that is afunction of the state and which is corrupted with noise.

The state of the system is the unknown position of an access point. TheEKF represents the state of the system by state equations at each timestep k and by output equations at each time step k. A state equation isgoverned by a difference equation or transition model, f(•) whichrelates the state at time step k−1 to the current step k. The stateequation at time step k is represented mathematically as follows:

x _(k) =f(x _(k-1))+w _(k-1)

where

x_(k) is the state at time step k,

f(•) is a state transition model (linear or non-linear) and

w_(k) is the process noise vector which is drawn from a Gaussiandistribution with zero mean with covariance Q_(k) (w_(k)˜N (0, Q_(k))),where Q_(k) is the process noise covariance matrix.

The output equation represents the measurement of the state of system attime step k as a function of a non-linear difference equation ortransition model, h(•), taking into consideration the measurement noise.The output equation at time step k is represented mathematically asfollows:

z _(k) =h(x _(k))+v _(k)

where

Z_(k) is the measurement of the state of x_(k),

h(•) is a non-linear measurement transition model which maps the statespace into the measurement space, and

V_(k) is the measurement noise which is drawn from a Gaussiandistribution with zero mean with covariance R_(k) (V_(k)˜N (0, R_(k))),where R_(k) is the measurement noise covariance matrix.

The state at time step k is represented as x_(k). The state x can berepresented mathematically as a vector, where each entry in the vectorrepresents the locations. For example, the state vector x can athree-dimensional vector represented by the tuple (latitude, longitude,altitude), a two-dimensional vector represented as (latitude,longitude), or any other representation used within a geographicalcoordinate system.

The Extended Kalman Filter consists of two phases: a predict phase andan update phase. In the predict phase, a predicted state estimate at thecurrent time step k is estimated from the previous time step k−1. Thepredicted state estimate does not include measurement data observed atthe current time step k. The second phase is an update phase where thepredicted state estimate is combined with the measurement data observedat the current time step to produce a refined state estimate. Thepredict phase and the update phase are repeated or reiterated for eachtime step over a time period until the state estimate converges within apredetermined error tolerance.

The predict phase generates the predicted state and its correspondingpredicted covariance as follows:

Predicted state {circumflex over (x)}_(k|k-1) =x _(k-1|k-1)  (1)

Predicted covariance P _(k|k-1) =P _(k-1|k-1) +Q _(k-1)  (2)

where

-   -   k=time step,    -   x_(k)=state at time step k,    -   Q_(k)=process noise covariance matrix at time step k.

The update phase generates the updated state and its covariance asfollows:

Innovation {tilde over (y)}_(k) =z _(k) −h({circumflex over (x)}_(k|k-1))  (3)

Innovation Covariance S _(k) =H _(k) P _(k|k-1) H _(k) ^(T) +R _(k)  (4)

Kalman Gain K _(k) =P _(k|k-1) H _(k) ^(T) S _(k) ⁻¹  (5)

Updated State {circumflex over (x)}_(k|k) ={circumflex over (x)}_(k|k-1) +K _(k) {tilde over (y)} _(k)  (6)

Updated Covariance P _(k|k)=(I−K _(k) H _(k))P _(k|k-1)  (7)

where

-   -   z_(k) is the measurement matrix and is represented        mathematically as z_(k)=h(x_(k))+v_(k), where v_(k) is the        measurement noise,    -   H_(k) is the Jacobian matrix of the partial derivatives of h        with respect to x and is represented mathematically as

${H_{k} = {\frac{\partial h}{\partial x}_{{\hat{x}}_{k{k - 1}}}}},$

-   -   R_(k) is the measurement noise covariance matrix, and    -   I is the identity matrix.

Attention now turns to a more detailed description of the use of the EKFin the self-positioning methodology.

Referring to FIG. 4, the self-positioning procedure 140 starts bydetermining whether the precise location of the access point is known(step 164). A precise location is a location that the access pointdetermined directly from a positioning system and not estimated based onposition data from its associated stations. In the event the preciselocation of the access point is known (step 164—Yes), the access pointcan participate in assisting its stations in their self-positioning(step 168) and in tracking for any movement from its location (step170). Steps 168 and 170 are described in more detail below.

When the precise location is unknown (step 164—No), the procedure 140embarks on determining its own location (step 166). Turning to FIG. 5,since the access point does not know its location, the access point isconfigured to suspend its participation in assisting its stations indetermining their location (step 180).

Next, an initial state estimate, {circumflex over (x)}₀, and thecorresponding initial state error covariance, P₀, is calculated in orderto initialize the EKF (step 182). In an embodiment, the initial stateestimate is determined from the locations of the location awarestations. In some embodiments, the location unaware access point canobtain these locations using the broadcast frames described in the U.S.patent application entitled, “Management-Packet Communication of GPSSatellite Positions”, assigned to QUALCOMM Incorporated, applicationSer. No. 12/840,155, filed on Jul. 20, 2010, which is herebyincorporated by reference.

In short, the information element of a beacon frame is used by a stationto broadcast to an access point within its reception range, thestation's position data and an associated quality estimate. Thisposition data can either be broadcasted at regular intervals throughbeacon frames. Alternatively, a probe request can be issued by eitherthe station or access point to obtain this position data and qualityestimate. A station receiving the probe request would reply with itsposition data and/or quality estimate.

In the case where the location unaware access point is associated withone location aware station at the initial time step, the location ofthat location aware station is used as the initial state estimate{circumflex over (x)}₀. The initial state error covariance can be basedon the quality estimate or the maximum range of the access point. In thecase where the access point is associated with more than one locationaware station, the initial state estimate can be a weighted average ofthe locations of the location aware stations. The weights can be basedon the received signal strength of the power of an incoming signal fromthe location aware station or any other signal quality measurement.Alternatively, the weights can be based on timing measurements from thelocation aware stations. Examples of such timing measurements arewithout limitation, the classic time of arrival (TOA) measurements,round trip time (RTT) measurements, and observed time difference (OTD)measurements. In another embodiment, the weights can be any combinationof the signal quality and/or timing measurements. In yet anotherembodiment where the location unaware access point is associated withmore than three location aware stations, the initial state estimate canbe determined using any one of the well known batch processedleast-squares techniques.

Next, the initial state estimate is refined by using the EKF to estimatethe location of the access point (step 184). FIG. 6 depicts the stepsused to estimate the location within a predetermined error tolerance.Referring to FIG. 6, the EKF iterates for each time step k until theprocess converges (step 190). At step 192, the EKF calculates apredicted location estimate and its covariance as described above withrespect to equations (1) and (2) above. Step 192 is the predict phase ofthe EKF which was described above.

In step 194, the EFK obtains measurement data from its location awarestations. In embodiments where the position is represented as the tuple(x, y), the measurement data can be represented mathematically asfollows:

$\begin{matrix}{z_{k_{i}} = {h_{i}( x_{k} )}} \\{= {\sqrt{( {x_{k} - x_{s_{i}}} )^{2} + ( {y_{k} - y_{s_{i}}} )^{2}} + v_{k_{i}}}}\end{matrix}$

where (x_(k), y_(k)) represents the coordinates of the location unawareaccess point at time k,

(x_(s) _(i) , y_(s) _(i) ) represents the coordinates of the station iat time k, for all i within range of the location unaware access point,and

v_(k) _(i) is the measurement noise which is assumed to be zero meanGaussian white noise with a variance R_(k) _(i) and the function h_(i)is differentiable with respect to the state.

The access point can obtain this measurement data in any one of a numberof ways. In some embodiments, the measurement data can be derived fromtiming measurements obtained from location aware stations. These timingmeasurements can be any one of the classic time of arrival (TOA)measurements, round trip time (RTT) measurements, and observed timedifference (OTD) measurements.

In some other embodiments, the range or distance measurement data can bederived from more advanced measurements such as those described in U.S.patent application entitled, Synchronization-Free Station Locator InWireless Network, assigned to Atheros Communications Inc., filed on Sep.3, 2009 with application Ser. No. 12/553,757, which is herebyincorporated by reference. In short, this patent application providesseveral different embodiments for determining the distance or range of astation from an access point. In some embodiments, the access pointsends a unicast to a station and notes its time of departure (TOD). Thestation receives the unicast packet and notes its time of arrivalTOA(D). The station sends an acknowledgment packet back to the accesspoint and notes its time of departure TOD(D_ACK). The access pointreceives the acknowledgment packet and notes its time of arrival,TOA(D_ACK). The distance between the access point and the station can bedetermined using a first difference between the TOA(D_ACK) and theTOD(D) and a second difference between the TOD(D_ACK) and the TOA(D).Other embodiments described in this patent application can also be usedto obtain the range measurement data in step 194.

Next in step 196, the EKF updates the predicted state estimate andcovariance with the measurement data as described above with respect toequations (3)-(7) above. Step 196 is the update phase of the EKF whichwas described above.

The process tests for convergence in step 198. The EKF converges whenthe difference between the state estimate at the current time step k andthe state estimate at the previous time step k−1 is within auser-defined error tolerance. Mathematically, the EKF converges when thefollowing condition occurs: |{circumflex over (x)}_(k)−{circumflex over(x)}_(k-1)|<ε, where ε is a user-defined error tolerance. If theconvergence criterion is not met, then the EKF proceeds through anotheriteration of steps 192-198 for the next time step. Otherwise, if theconvergence criterion is satisfied, then the current state estimate isdeemed the location of the access point.

Referring back to FIG. 5, the procedure 140 determines a test statisticfor use in detecting the movement of the access point after its locationhas been determined (step 186). In general, the test statistic can bedefined mathematically, as follows:

t _(k) ={tilde over (y)} _(k) ^(T) S _(k) ⁻¹ {tilde over (y)} _(k),  (8)

where k is the time step of convergence.

Referring back to FIG. 4, once the location unaware access point knowsits location, the location unaware access point can participate inassisting other stations in their self-positioning determinations (step170). The location unaware access point can assist other stations oraccess points by either broadcasting its location or by responding toprobe requests with its location. As noted above, a location unawareaccess point transmits beacon frames at regular intervals. In thesebeacon frames, the location unaware access point's position data andquality estimate can be transmitted. Stations within the reception rangeof the beacon frame can receive the location unaware access point'sposition and quality estimate. Alternatively, the location unawareaccess point can respond to a probe request requesting its position.

In addition, the location unaware access point tracks for any movementwhich would indicate that the location unaware access point needs tore-determine its position (step 170). In some embodiments, monitoringthe movement of the location is performed using chi-square testing. Inusing this technique, the procedure 140 is always testing a nullhypothesis which states that there is no significant difference betweenthe test statistic (i.e., expected state) and the current measuredstate. This testing is performed by determining whether the teststatistic, t_(k), falls in a confidence region (1−α) for a Chi-squarerandom variable with n_(k) degrees of freedom. The confidence level, α,is a user-defined parameter. Given a user-defined threshold, χ² _(α),for a particular confidence level α, the hypothesis test tests where thetest statistic t_(k) lies relative to the user-defined threshold, χ²_(α). The absence of movement is indicated when t_(k)<χ² _(α) andmovement is detected when t_(k)>χ² _(α).

Referring to FIG. 7, in step 202, the hypothesis test is performed onthe test statistic by determining whether t_(k)<χ² _(α) or t_(k)>χ²_(α). In the event movement is not detected (step 204—No), the procedure170 tests the hypothesis again (step 202). This test can be performedrandomly, continuously, or at user-defined intervals.

In the event movement is detected (step 204—Yes), the procedure 170confirms that the movement has occurred (step 206). A single teststatistic at time k may not be sufficient to confirm that the locationunaware access point has been displaced. Confirmation of the movementcan be determined by formulating the test statistic as a combination ofseveral test statistics within a time interval [k, k+N−1], where N isthe number of additional time steps, and k is the time step at which thehypothesis test first failed. This new test statistic can be the averageof the test statistics taken within the time interval [k, k+N−1]. Instep 208, confirmation of the movement is determined by testing theaverage of the test statistics taken within the time interval [k, k+N−1]relative to the user-defined threshold χ² _(α), for a particularconfidence level α. If movement is confirmed (step 208—Yes), then a newlocation is determined in the manner explained above (step 166).Otherwise, if the movement is not confirmed (step 208—No), then theprocedure 170 continues to track for any other movements (step 202).

The process described above is repeated continuously during theoperation of the access point. Alternatively, the process can be invokedto execute at predetermined time intervals to track the location of theaccess point and to re-compute the location as needed. In the event theaccess point is powered down, the process will restart from thebeginning.

In some embodiments, the location unaware access point may utilize alocation server 108 to obtain and store the locations of the locationaware stations in a reference database 158. The location server 108 canuse any of the techniques described herein in addition to otherwell-known techniques to obtain the locations of the location awarestations. In addition, the location server 108 can be used to track themovement of these stations using the techniques described herein orthrough other means and store the updated locations in the referencedatabase 158. The self-positioning location procedure 140 can obtain thelocations and/or measurement data from the location server 108.

In some embodiments, an access point 102 may be equipped with asatellite receiver but may not be using the satellite data to determineits location. The access point 102 can be buried in an indoor locationwhere the satellite signal reception is blocked or can be experiencingother problems that affects the reception of the satellite data. Inthese situations, the access point 102 may employ the technologydescribed herein to learn its location and to continuously refine itslocation based on the locations of its location aware stations.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the invention and in practical applications, to therebyenable others skilled in the art to best utilize the invention andvarious embodiments with various modifications as are suited to theparticular use contemplated. It is intended that the scope of theinvention be defined by the following claims and their equivalents.

We claim:
 1. A system comprising: one or more location aware stations,each having a known location; a location unaware access point inwireless communication with the one or more location aware stations, thelocation unaware access point having an unknown location, wherein thelocation unaware access point includes a non-transitory computerreadable medium that store instructions, which when executed, cause thelocation unaware access point to: determine a coarse location estimatefor the location unaware access point based on one or more knownlocations of the one or more location aware stations; refine the coarselocation estimate based on range measurement data obtained from the oneor more location aware stations over one or more time periods until therefined coarse location estimate meets an error tolerance; and use therefined coarse location estimate as a determined location of thelocation unaware access point.
 2. The system of claim 1, wherein theinstructions further cause the location unaware access point to estimatethe coarse location estimate based on a weighted average of knownlocations of the one or more location aware stations.
 3. The system ofclaim 2, wherein the weighted average is based on received signalstrength indicators or timing measurements associated with signalsreceived by the location unaware access point from the one or morelocation aware stations.
 4. The system of claim 1, wherein the locationunaware access point further comprises an extended Kalman filter (EKF),which refines the coarse location estimate at each of the one or moretime periods until the refined coarse location estimate meets the errortolerance.
 5. The system of claim 1, wherein the instructions furthercause the location unaware access point to: track a current location ofthe location unaware access point relative to current locations of theone or more location aware stations and the determined location of thelocation unaware access point; determine that the location unawareaccess point has moved from the determined location; and calculate a newlocation of the location unaware access point.
 6. The system of claim 5,wherein the instructions further cause the location unaware access pointto confirm movement of the location unaware access point from thedetermined location by sampling range measurements associated with theone or more location aware stations over a second time period.
 7. Thesystem of claim 1, wherein the instructions further cause the locationunaware access point to: determine a test statistic associated with thedetermined location of the location unaware access point; use the teststatistic to detect movement of the location unaware access point fromthe determined location; and confirm the movement of the locationunaware access point from the determined location by formulating thetest statistic as a combination of several test statistics within a timeinterval.
 8. The system of claim 7, wherein movement of the locationunaware access point from the determined location is detected bycomparing the test statistic to a threshold based on a confidence level.9. The system of claim 1, wherein each known location is a positionwithin one of a satellite positioning system and a wirelesscommunication positioning system.
 10. An access point comprising: awireless receiver for receiving location and measurement data from oneor more location aware stations; and a processor coupled to the wirelessreceiver, wherein the processor is configured to iteratively determinelocation estimates of the access point over a plurality of time stepsbased on the location and measurement data received from the one or morelocation aware stations, wherein the processor determines a location ofthe access point based on successive location estimates differing byless than a threshold.
 11. The access point of claim 10, wherein theprocessor is configured to determine an initial one of the locationestimates based on a weighted average of the location and measurementdata received from the one or more location aware stations.
 12. Theaccess point of claim 11, wherein the processor is configured to weightthe received location and measurement data based on signal qualityassociated with the received location and measurement data.
 13. Theaccess point of claim 11, wherein the processor is configured to weightthe received location and measurement data based on timing measurementsassociated with the received location and measurement data.
 14. Theaccess point of claim 10, wherein the processor utilizes an extendedKalman filter (EKE) to determine the location estimates.
 15. The accesspoint of claim 14, wherein the extended Kalman filter is configured tocalculate covariances associated with the location estimates.
 16. Theaccess point of claim 10, wherein the threshold comprises a user-definederror tolerance.
 17. The access point of claim 10, further comprising awireless transmitter coupled to the processor, wherein the processor isconfigured to transmit the determined location of the access point tothe wireless transmitter.
 18. The access point of claim 17, wherein theprocessor includes the determined location of the access point in beaconframes transmitted to the wireless transmitter.
 19. The access point ofclaim 10, wherein the processor is configured to monitor movement of theaccess point from the determined location of the access point.
 20. Theaccess point of claim 19, wherein the processor is configured to confirma detected movement of the access point from the determined location ofthe access point over a plurality of time steps.
 21. The access point ofclaim 19, wherein the processor is configured to monitor movement of thedetermined location of the access point in response to the receivedlocation and measurement data.